Rules-based training of federated machine learning models

ABSTRACT

Approaches presented herein enable training a federated machine learning model. More specifically, data is received from one or more sensors associated with an edge device. In response to the data exceeding a pre-determined threshold, a hazardous condition is identified. The hazardous condition is classified as valid or invalid, and in response to the hazardous condition being classified as valid, the hazardous condition and the data from one or more sensors are applied to the federated machine learning model.

TECHNICAL FIELD

The present invention relates generally to training federated machinelearning models and, more specifically, to using rules to classifysensor data generated on edge devices, such that the classified sensordata is used to train federated machine learning models.

BACKGROUND

Federated machine learning models enable distributed training of amachine learning model across multiple decentralized edge devices orservers, each having disparate datasets. This approach differs fromconventional centralized machine learning techniques where localdatasets are uploaded to a single system. Federated machine learningmodels allow multiple actors to build a consolidated machine learningmodel without sharing data, thus addressing issues such as data privacy,data security, data access rights, and access to heterogeneous data.

SUMMARY

Approaches presented herein enable training a federated machine learningmodel. More specifically, data is received from one or more sensorsassociated with an edge device. In response to the data exceeding apre-determined threshold, a hazardous condition is identified. Thehazardous condition is classified as valid or invalid, and in responseto the hazardous condition being classified as valid, the hazardouscondition and the data from one or more sensors are applied to thefederated machine learning model.

One aspect of the present invention includes a method for training afederated machine learning model, the method comprising: receiving datafrom one or more sensors associated with an edge device, responsive tothe data exceeding a pre-determined threshold, identifying a hazardouscondition, classifying the hazardous condition as valid or invalid, andresponsive to classifying the hazardous condition as valid, applying thehazardous condition and the data from one or more sensors to thefederated machine learning model.

Another aspect of the present invention includes a computer system fortraining a federated machine learning model, the computer systemcomprising: a memory medium comprising program instructions, a buscoupled to the memory medium, and a processor, for executing the programinstructions, coupled to a federated machine learning model trainingengine via the bus that when executing the program instructions causesthe system to: receive data from one or more sensors associated with anedge device, responsive to the data exceeding a pre-determinedthreshold, identify a hazardous condition, classify the hazardouscondition as valid or invalid, and responsive to classifying thehazardous condition as valid, apply the hazardous condition and the datafrom one or more sensors to the federated machine learning model.

Yet another aspect of the present invention includes a computer programproduct for training a federated machine learning model, the computerprogram product comprising a computer readable hardware storage device,and program instructions stored on the computer readable hardwarestorage device, to: receive data from one or more sensors associatedwith an edge device, responsive to the data exceeding a pre-determinedthreshold, identify a hazardous condition, classify the hazardouscondition as valid or invalid, and responsive to classifying thehazardous condition as valid, apply the hazardous condition and the datafrom one or more sensors to the federated machine learning model.

Still yet, any of the components of the present invention could bedeployed, managed, serviced, etc., by a service provider who offers toimplement training a federated machine learning model in a computersystem.

Embodiments of the present invention also provide related systems,methods, and/or program products.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of this invention will be more readilyunderstood from the following detailed description of the variousaspects of the invention taken in conjunction with the accompanyingdrawings in which:

FIG. 1 shows an architecture in which the invention may be implementedaccording to illustrative embodiments.

FIG. 2 shows a system diagram describing the functionality discussedherein according to illustrative embodiments.

FIG. 3 shows a process workflow of a user for training a federatedmachine learning model according to illustrative embodiments.

FIG. 4 shows a process workflow of a supervisor for training a federatedmachine learning model according to illustrative embodiments.

FIG. 5 shows a process flowchart for training a federated machinelearning model according to illustrative embodiments.

The drawings are not necessarily to scale. The drawings are merelyrepresentations, not intended to portray specific parameters of theinvention. The drawings are intended to depict only typical embodimentsof the invention, and therefore should not be considered as limiting inscope. In the drawings, like numbering represents like elements.

DETAILED DESCRIPTION

Illustrative embodiments will now be described more fully herein withreference to the accompanying drawings, in which illustrativeembodiments are shown. It will be appreciated that this disclosure maybe embodied in many different forms and should not be construed aslimited to the illustrative embodiments set forth herein. Rather, theseembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this disclosure to thoseskilled in the art.

Furthermore, the terminology used herein is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of this disclosure. As used herein, the singular forms “a”,“an”, and “the” are intended to include the plural forms as well, unlessthe context clearly indicates otherwise. Furthermore, the use of theterms “a”, “an”, etc., do not denote a limitation of quantity, butrather denote the presence of at least one of the referenced items.Furthermore, similar elements in different figures may be assignedsimilar element numbers. It will be further understood that the terms“comprises” and/or “comprising”, or “includes” and/or “including”, whenused in this specification, specify the presence of stated features,regions, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,regions, integers, steps, operations, elements, components, and/orgroups thereof.

Unless specifically stated otherwise, it may be appreciated that termssuch as “processing,” “detecting,” “determining,” “evaluating,”“receiving,” or the like, refer to the action and/or processes of acomputer or computing system, or similar electronic data center device,that manipulates and/or transforms data represented as physicalquantities (e.g., electronic) within the computing system's registersand/or memories into other data similarly represented as physicalquantities within the computing system's memories, registers or othersuch information storage, transmission or viewing devices. Theembodiments are not limited in this context.

As stated above, embodiments described herein provide for training afederated machine learning model. More specifically, data is receivedfrom one or more sensors associated with an edge device. In response tothe data exceeding a pre-determined threshold, a hazardous condition isidentified. The hazardous condition is classified as valid or invalid,and in response to the hazardous condition being classified as valid,the hazardous condition and the data from one or more sensors areapplied to the federated machine learning model.

Referring now to FIG. 1, a computerized implementation 10 of anembodiment for training a federated machine learning model will be shownand described. Computerized implementation 10 is only one example of asuitable implementation and is not intended to suggest any limitation asto the scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, computerized implementation 10 is capableof being implemented and/or performing any of the functionality setforth hereinabove.

In computerized implementation 10, there is a computer system/server 12,which is operational with numerous other (e.g., special purpose)computing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

This is intended to demonstrate, among other things, that the presentinvention could be implemented within a network environment (e.g., theInternet, a wide area network (WAN), a local area network (LAN), avirtual private network (VPN), etc.), a cloud computing environment, acellular network, or on a stand-alone computer system. Communicationthroughout the network can occur via any combination of various types ofcommunication links. For example, the communication links can compriseaddressable connections that may utilize any combination of wired and/orwireless transmission methods. Where communications occur via theInternet, connectivity could be provided by conventional TCP/IPsockets-based protocol, and an Internet service provider could be usedto establish connectivity to the Internet. Still yet, computersystem/server 12 is intended to demonstrate that some or all of thecomponents of implementation 10 could be deployed, managed, serviced,etc., by a service provider who offers to implement, deploy, and/orperform the functions of the present invention for others.

Computer system/server 12 is intended to represent any type of computersystem that may be implemented in deploying/realizing the teachingsrecited herein. Computer system/server 12 may be described in thegeneral context of computer system/server executable instructions, suchas program modules, being executed by a computer system. Generally,program modules may include routines, programs, objects, components,logic, data structures, and so on, that perform particular tasks orimplement particular abstract data types. In this particular example,computer system/server 12 represents an illustrative system for traininga federated machine learning model. It should be understood that anyother computers implemented under the present invention may havedifferent components/software, but can perform similar functions.

Computer system/server 12 in computerized implementation 10 is shown inthe form of a computing device. The components of computer system/server12 may include, but are not limited to, one or more processors orprocessing units 16, a system memory 28, and a bus 18 that couplesvarious system components including system memory 28 to processing unit16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Processing unit 16 refers, generally, to any apparatus that performslogic operations, computational tasks, control functions, etc. Aprocessor may include one or more subsystems, components, and/or otherprocessors. A processor will typically include various logic componentsthat operate using a clock signal to latch data, advance logic states,synchronize computations and logic operations, and/or provide othertiming functions. During operation, processing unit 16 collects androutes signals representing inputs and outputs between external devices14 and input devices (not shown). The signals can be transmitted over aLAN and/or a WAN (e.g., T1, T3, 56 kb, X.25), broadband connections(ISDN, Frame Relay, ATM), wireless links (802.11, Bluetooth, etc.), andso on. In some embodiments, the signals may be encrypted using, forexample, trusted key-pair encryption. Different systems may transmitinformation using different communication pathways, such as Ethernet orwireless networks, direct serial or parallel connections, USB,Firewire®, Bluetooth®, or other proprietary interfaces. (Firewire is aregistered trademark of Apple Computer, Inc. Bluetooth is a registeredtrademark of Bluetooth Special Interest Group (SIG)).

In general, processing unit 16 executes computer program code, such asprogram code for training a federated machine learning model, which isstored in memory 28, storage system 34, and/or program/utility 40. Whileexecuting computer program code, processing unit 16 can read and/orwrite data to/from memory 28, storage system 34, and program/utility 40.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random-access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia, (e.g., VCRs, DVRs, RAID arrays, USB hard drives, optical diskrecorders, flash storage devices, and/or any other data processing andstorage elements for storing and/or processing data). By way of exampleonly, storage system 34 can be provided for reading from and writing toa non-removable, non-volatile magnetic media (not shown and typicallycalled a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and/or an optical disk drive for reading fromor writing to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM, or other optical media can be provided. In such instances, eachcan be connected to bus 18 by one or more data media interfaces. As willbe further depicted and described below, memory 28 may include at leastone program product having a set (e.g., at least one) of program modulesthat are configured to carry out the functions of embodiments of theinvention.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium including, but not limited to, wireless,wireline, optical fiber cable, radio frequency (RF), etc., or anysuitable combination of the foregoing.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation. Memory28 may also have an operating system, one or more application programs,other program modules, and program data. Each of the operating system,one or more application programs, other program modules, and programdata or some combination thereof, may include an implementation of anetworking environment. Program modules 42 generally carry out thefunctions and/or methodologies of embodiments of the invention asdescribed herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a consumer to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via I/O interfaces22. Still yet, computer system/server 12 can communicate with one ormore networks such as a local area network (LAN), a general wide areanetwork (WAN), and/or a public network (e.g., the Internet) via networkadapter 20. As depicted, network adapter 20 communicates with the othercomponents of computer system/server 12 via bus 18. It should beunderstood that although not shown, other hardware and/or softwarecomponents could be used in conjunction with computer system/server 12.Examples include, but are not limited to: microcode, device drivers,redundant processing units, external disk drive arrays, RAID systems,tape drives, and data archival storage systems, etc.

The inventors of the present invention have found that gathering rawsensor data necessary for training a federated machine learning modelmay present some privacy issues with respect to sensitive personalinformation. An additional challenge is providing proper and correctclassification and labeling of data needed to implement the training ofa federated machine learning model.

Accordingly, the inventors of the present invention have developed asystem that anonymizes data used to train a federated machine learningmodel on an edge device when the federated model is contributed to acentral machine learning model in the cloud. The present invention alsoprovides a system to classify and label data that is used to train afederated machine learning model.

Referring now to FIG. 2, a system diagram describing the functionalitydiscussed herein according to an embodiment of the present invention isshown. It is understood that the teachings recited herein may bepracticed within any type of computing environment, including, but notlimited to, a networked computing environment (e.g., a cloud computingenvironment 50). A stand-alone computer system/server 12, in one examplean edge computer system, is shown in FIG. 2 for illustrative purposesonly. In the event the teachings recited herein are practiced in anetworked computing environment, each client need not have a federatedmachine learning model training engine 60 (hereinafter “system 60”).Rather, all or part of system 60 could be loaded on a server orserver-capable device that communicates (e.g., wirelessly) with theclients to provide for training a federated machine learning model.Regardless, as depicted, system 60 is shown within computersystem/server 12. In general, system 60 can be implemented asprogram/utility 40 on computer system/server 12 of FIG. 1 and can enablethe functions recited herein.

Along these lines, system 60 may perform multiple functions.Specifically, among other functions, system 60 can train a federatedmachine learning model in a networked computing environment. Toaccomplish this, system 60 can include a set of components (e.g.,program modules 42 of FIG. 1) for carrying out embodiments of thepresent invention. These components can include, but are not limited to,federated machine learning model 62, sensor data receiver 64, hazardouscondition identifier 66, hazardous condition classifier 68, and machinelearning model data applier 70.

Through computer system/server 12, system 60 may receive sensor datafrom one or more sensors 90 ₁-90 _(n) which may comprise, for example,at least one of: an accelerometer, a gyroscopic sensor, a biometricsensor, an air quality sensor, a video imaging sensor, a radar/proximitysensor, and a microphone. System 60 may communicate with central machinelearning model 100 so as to perform a consolidation of federated machinelearning model 62 with model 100.

In one embodiment, system 60 may be implemented as an edge computingdevice, for example a smartphone. System 60 may be used by user 210, inone example an employee, and/or supervisor 220, in one example anemployee supervisor of user 210, to train federated machine learningmodel 62. As mentioned above, system 60 may receive sensor data from oneor more sensors 90 ₁-90 _(n) using sensor data receiver 64 whichperforms functions related to receiving data, for example, convertinganalog readings received from sensors to digital values.

Responsive to the data received by sensor data receiver 64 from one ormore sensors 90 ₁-90 _(n) exceeding a pre-determined threshold,hazardous condition identifier 66 may identify a hazardous condition.The pre-determined threshold for hazardous condition identifier 66 toidentify a hazardous condition may be determined and set based on asliding window of values, i.e. values falling between a set of boundaryconditions, as well as other known algorithms. The hazardous conditionsidentified by hazardous condition identifier 66 may comprise, forexample, at least one of: a fall event, an impact event, excessive heartrate, elevated blood pressure, lowered body temperature, elevated bodytemperature, elevated stress level, excessive fatigue, reduced oxygenlevel, elevated noxious gas level, and elevated sound level. In oneembodiment, hazardous condition identifier 66 may be implemented usingone or more rules, where identifying a hazardous condition is performedby meeting one or more conditions defined in a rule of the one or morerules, and the rule is executed by a rules-engine.

The one or more rules are also known as “shields”, for example: fallshield, air quality shield, vibration shield, noise shield, etc. Forexample, a pre-determined threshold of 100 dB may be set for the noiseshield, and if a noise above that sound level is detected by a sensor ofone or more sensors 90 ₁-90 _(n), hazardous condition identifier 66 mayidentify a hazardous noise level that could potentially cause a hearingloss from prolonged exposure.

If hazardous condition identifier 66 identifies a hazardous condition,hazardous condition classifier 68 prompts user 210 and/or supervisor 220to enter or indicate a classification of the identified hazardouscondition. Hazardous condition classifier 68 classifies the identifiedhazardous condition as valid or invalid based upon input received fromuser 210 and/or supervisor 220. For example, if the identified hazardouscondition is a false positive, then user 210 and/or supervisor 220 mayindicate that hazardous condition classifier 68 should classify theidentified hazardous condition as invalid. Alternatively, if theidentified hazardous condition was correctly identified, then user 210and/or supervisor 220 may indicate that hazardous condition classifier68 should classify the identified hazardous condition as valid.

In response to hazardous condition classifier 68 classifying theidentified hazardous condition as valid, machine learning model dataapplier 70 may apply the identified hazardous condition and the datafrom one or more sensors to federated machine learning model 62. Theidentified hazardous condition and the data from one or more sensors arealso known as “labeled data” or a “label”. Alternatively, in response tohazardous condition classifier 68 classifying the identified hazardouscondition as invalid, machine learning model data applier 70 may discardthe identified hazardous condition and the data from one or moresensors. If the data applied to federated machine learning model 62 bymachine learning model data applier 70 is properly and fully classified,system 60 may communicate with central machine learning model 100 so asto perform a consolidation of federated machine learning model 62 withcentral machine learning model 100 using machine learning model dataapplier 70. Alternatively, if the data applied to federated machinelearning model 62 by machine learning model data applier 70 is notproperly and fully classified, machine learning model 62 may bediscarded by machine learning model data applier 70.

Referring now to FIG. 3 in connection with FIG. 2, an illustrativeembodiment, process workflow 300, is depicted that may be used by user210 (shown in FIG. 2) to train federated machine learning model 62(shown in FIG. 2). At 302, a user session starts. At 304, sensortime-series data is received sensor data receiver 64 (shown in FIG. 2)from one or more sensors 90 ₁-90 _(n) (shown in FIG. 2). At 306,federated machine learning model 62 (shown in FIG. 2) is trained usingsensor data from one or more sensors 90 ₁-90 _(n) (shown in FIG. 2) andhazardous condition identifier 66 (shown in FIG. 2), implemented usingone or more rules that are executed by a rules-engine, monitors a dataflow from sensor data receiver 64 (shown in FIG. 2). At 308, the usersession ends.

At 310, it is determined if a rule of hazardous condition identifier 66(shown in FIG. 2) was triggered. If “no”, additional data received fromsensor data receiver 64 (shown in FIG. 2) is run through hazardouscondition identifier 66 (shown in FIG. 2) at 310. If “yes”, at 312 user210 (shown in FIG. 2) is notified and prompted by hazardous conditionclassifier 68 (shown in FIG. 2) to optionally indicate a classificationfor an event identified by hazardous condition identifier 66 (shown inFIG. 2). At 314, it is determined if the event identified by hazardouscondition identifier 66 (shown in FIG. 2) is an actual incident. If“no”, the event or incident is discarded at 320. If “yes”, at 318, user210 (shown in FIG. 2) confirms that this event was an actual incident,and the incident is stored as such in incident database 330. If user 210(shown in FIG. 2) does not indicate a classification, at 316, the eventis stored in incident database 330 as an incident needing review.

Referring now to FIG. 4 in connection with FIG. 2, an illustrativeembodiment, process workflow 400, is depicted that may be used bysupervisor 220 (shown in FIG. 2) to train federated machine learningmodel 62 (shown in FIG. 2). At 402, a session review starts. At 404,supervisor 220 (shown in FIG. 2) reviews incidents from a session thatare stored in incident database 330. At 406, it is determined if anincident is already classified as an actual incident. If “yes”, at 418,it is determined if all incidents have been reviewed. If “yes”, at 420,machine learning model data applier 70 (shown in FIG. 2) sends federatedmachine learning model 62 (shown in FIG. 2) for consolidation withcentral machine learning model 100. If “no”, supervisor 220 (shown inFIG. 2) continues at 404 to review reported incidents from a sessionthat are stored in incident database 330.

Returning now to 406, if there is a “no” condition branch to 408,supervisor 220 (shown in FIG. 2) ascertains if an unclassified incidentis an actual incident. At 410, it is determined if the incident can beclassified. If “no”, federated machine learning model 62 (shown in FIG.2) is discarded at 412. If “yes”, the incident is marked as an actualincident at 414, and is added to incident database 330.

Referring now to FIG. 5, in one embodiment, a system (e.g., computersystem/server 12) carries out the methodologies disclosed herein. Shownis a process flowchart 500 for training a federated machine learningmodel. At 502, data is received from one or more sensors associated withan edge device. At 504, in response to the data exceeding apre-determined threshold, a hazardous condition is identified. At 506,the hazardous condition is classified as valid or invalid. At 508, inresponse to the hazardous condition being classified as valid, thehazardous condition and the data from one or more sensors are applied tothe federated machine learning model.

Some of the functional components described in this specification havebeen labeled as systems or units in order to more particularly emphasizetheir implementation independence. For example, a system or unit may beimplemented as a hardware circuit comprising custom VLSI circuits orgate arrays, off-the-shelf semiconductors such as logic chips,transistors, or other discrete components. A system or unit may also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices, orthe like. A system or unit may also be implemented in software forexecution by various types of processors. A system or unit or componentof executable code may, for instance, comprise one or more physical orlogical blocks of computer instructions, which may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified system or unit need not be physicallylocated together, but may comprise disparate instructions stored indifferent locations which, when joined logically together, comprise thesystem or unit and achieve the stated purpose for the system or unit.

Further, a system or unit of executable code could be a singleinstruction, or many instructions, and may even be distributed overseveral different code segments, among different programs, and acrossseveral memory devices. Similarly, operational data may be identifiedand illustrated herein within modules, and may be embodied in anysuitable form and organized within any suitable type of data structure.The operational data may be collected as a single data set, or may bedistributed over different locations including over different storagedevices and disparate memory devices.

Furthermore, systems/units may also be implemented as a combination ofsoftware and one or more hardware devices. For instance, program/utility40 may be embodied in the combination of a software executable codestored on a memory medium (e.g., memory storage device). In a furtherexample, a system or unit may be the combination of a processor thatoperates on a set of operational data.

As noted above, some of the embodiments may be embodied in hardware. Thehardware may be referenced as a hardware element. In general, a hardwareelement may refer to any hardware structures arranged to perform certainoperations. In one embodiment, for example, the hardware elements mayinclude any analog or digital electrical or electronic elementsfabricated on a substrate. The fabrication may be performed usingsilicon-based integrated circuit (IC) techniques, such as complementarymetal oxide semiconductor (CMOS), bipolar, and bipolar CMOS (BiCMOS)techniques, for example. Examples of hardware elements may includeprocessors, microprocessors, circuits, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), logic gates, registers, semiconductor devices, chips,microchips, chip sets, and so forth. However, the embodiments are notlimited in this context.

Any of the components provided herein can be deployed, managed,serviced, etc., by a service provider that offers to deploy or integratecomputing infrastructure with respect to a process for training afederated machine learning model. Thus, embodiments herein disclose aprocess for supporting computer infrastructure, comprising integrating,hosting, maintaining, and deploying computer-readable code into acomputing system (e.g., computer system/server 12), wherein the code incombination with the computing system is capable of performing thefunctions described herein.

In another embodiment, the invention provides a method that performs theprocess steps of the invention on a subscription, advertising, and/orfee basis. That is, a service provider, such as a Solution Integrator,can offer to create, maintain, support, etc., a process for training afederated machine learning model. In this case, the service provider cancreate, maintain, support, etc., a computer infrastructure that performsthe process steps of the invention for one or more customers. In return,the service provider can receive payment from the customer(s) under asubscription and/or fee agreement, and/or the service provider canreceive payment from the sale of advertising content to one or morethird parties.

Also noted above, some embodiments may be embodied in software. Thesoftware may be referenced as a software element. In general, a softwareelement may refer to any software structures arranged to perform certainoperations. In one embodiment, for example, the software elements mayinclude program instructions and/or data adapted for execution by ahardware element, such as a processor. Program instructions may includean organized list of commands comprising words, values, or symbolsarranged in a predetermined syntax that, when executed, may cause aprocessor to perform a corresponding set of operations.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It is apparent that there has been provided herein approaches totraining a federated machine learning model. While the invention hasbeen particularly shown and described in conjunction with exemplaryembodiments, it will be appreciated that variations and modificationswill occur to those skilled in the art. Therefore, it is to beunderstood that the appended claims are intended to cover all suchmodifications and changes that fall within the true spirit of theinvention.

What is claimed is:
 1. A computer-implemented method for training afederated machine learning model, the method comprising: receiving datafrom one or more sensors associated with an edge device; responsive tothe data exceeding a pre-determined threshold, identifying a hazardouscondition; classifying the hazardous condition as valid or invalid; andresponsive to classifying the hazardous condition as valid, applying thehazardous condition and the data from one or more sensors to thefederated machine learning model.
 2. The computer-implemented method ofclaim 1, further comprising: responsive to classifying the hazardouscondition as invalid, discarding the hazardous condition and the datafrom one or more sensors.
 3. The computer-implemented method of claim 1,further comprising consolidating the federated machine learning modelwith a central machine learning model.
 4. The computer-implementedmethod of claim 1, wherein the identifying a hazardous condition isperformed by meeting one or more conditions defined in a rule, andwherein the rule is executed by a rules-engine.
 5. Thecomputer-implemented method of claim 4, wherein the hazardous conditioncomprises at least one of: a fall event, an impact event, excessiveheart rate, elevated blood pressure, lowered body temperature, elevatedbody temperature, elevated stress level, excessive fatigue, reducedoxygen level, elevated noxious gas level, and elevated sound level. 6.The computer-implemented method of claim 1, wherein the classifying thehazardous condition as valid or invalid is based upon input receivedfrom a user of the edge device, and wherein the user of the edge deviceis at least one of: an employee, and a supervisor.
 7. Thecomputer-implemented method of claim 1, wherein the one or more sensorscomprise at least one of: an accelerometer, a gyroscopic sensor, abiometric sensor, an air quality sensor, a video imaging sensor, aradar/proximity sensor, and a microphone.
 8. A computer system fortraining a federated machine learning model, the computer systemcomprising: a memory medium comprising program instructions; a buscoupled to the memory medium; and a processor, for executing the programinstructions, coupled to a federated machine learning model trainingengine via the bus that when executing the program instructions causesthe system to: receive data from one or more sensors associated with anedge device; responsive to the data exceeding a pre-determinedthreshold, identify a hazardous condition; classify the hazardouscondition as valid or invalid; and responsive to classifying thehazardous condition as valid, apply the hazardous condition and the datafrom one or more sensors to the federated machine learning model.
 9. Thecomputer system of claim 8, the instructions further causing the systemto: responsive to classifying the hazardous condition as invalid,discard the hazardous condition and the data from one or more sensors.10. The computer system of claim 8, the instructions further causing thesystem to consolidate the federated machine learning model with acentral machine learning model.
 11. The computer system of claim 8,wherein the instructions to identify a hazardous condition are performedby meeting one or more conditions defined in a rule, and wherein therule is executed by a rules-engine.
 12. The computer system of claim 11,wherein the hazardous condition comprises at least one of: a fall event,an impact event, excessive heart rate, elevated blood pressure, loweredbody temperature, elevated body temperature, elevated stress level,excessive fatigue, reduced oxygen level, elevated noxious gas level, andelevated sound level.
 13. The computer system of claim 8, wherein theinstructions to classify the hazardous condition as valid or invalidutilize input received from a user of the edge device, and wherein theuser of the edge device is at least one of: an employee, and asupervisor.
 14. The computer system of claim 8, wherein the one or moresensors comprise at least one of: an accelerometer, a gyroscopic sensor,a biometric sensor, an air quality sensor, a video imaging sensor, aradar/proximity sensor, and a microphone.
 15. A computer program productfor training a federated machine learning model, the computer programproduct comprising a computer readable hardware storage device, andprogram instructions stored on the computer readable hardware storagedevice, to: receive data from one or more sensors associated with anedge device; responsive to the data exceeding a pre-determinedthreshold, identify a hazardous condition; classify the hazardouscondition as valid or invalid; and responsive to classifying thehazardous condition as valid, apply the hazardous condition and the datafrom one or more sensors to the federated machine learning model. 16.The computer program product of claim 15, the computer readable storagedevice further comprising instructions to: responsive to classifying thehazardous condition as invalid, discard the hazardous condition and thedata from one or more sensors.
 17. The computer program product of claim15, the computer readable storage device further comprising instructionsto consolidate the federated machine learning model with a centralmachine learning model.
 18. The computer program product of claim 15,wherein the program instructions to identify a hazardous condition areperformed by meeting one or more conditions defined in a rule, andwherein the rule is executed by a rules-engine, and wherein thehazardous condition comprises at least one of: a fall event, an impactevent, excessive heart rate, elevated blood pressure, lowered bodytemperature, elevated body temperature, elevated stress level, excessivefatigue, reduced oxygen level, elevated noxious gas level, and elevatedsound level.
 19. The computer program product of claim 15, wherein theprogram instructions to classify the hazardous condition as valid orinvalid utilize input received from a user of the edge device, andwherein the user of the edge device is at least one of: an employee, anda supervisor.
 20. The computer program product of claim 15, wherein theone or more sensors comprise at least one of: an accelerometer, agyroscopic sensor, a biometric sensor, an air quality sensor, a videoimaging sensor, a radar/proximity sensor, and a microphone.